Uplift modeling, also known as incremental modeling, is a predictive modeling technique that directly models the incremental impact of a treatment (such as direct marketing action) on an individual's behavior. Uplift modeling uses a control to not only measure the effectiveness of a marketing action but also to build a predictive model that predicts the incremental response to the marketing action. Currently uplift models are developed are developed by either looking at test and control populations separately, or by using one model with the treatment effect coded as a dummy variable and all possible interaction effects included in the model. These methodologies have several shortcomings, such as inefficient use of available data, and an inability to capture complex interactions between independent variables thereby limiting use to binary outputs.